Efficiency adjustable speech recognition system
US11715462B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Apr 29, 2021 |
| Grant date | Aug 1, 2023 |
| Priority date | — |
| Expiry date | Jan 13, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG10L15/22
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A computing system is configured to generate a transformer-transducer-based deep neural network. The transformer-transducer-based deep neural network comprises a transformer encoder network and a transducer predictor network. The transformer encoder network has a plurality of layers, each of which includes a multi-head attention network sublayer and a feed-forward network sublayer. The computing system trains an end-to-end (E2E) automatic speech recognition (ASR) model, using the transformer-transducer-based deep neural network. The E2E ASR model has one or more adjustable hyperparameters that are configured to dynamically adjust an efficiency or a performance of E2E ASR model when the E2E ASR model is deployed onto a device or executed by the device.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.